Random Forest Vs XGBoost Tree Based Algorithms

Random Forest Vs XGBoost Tree Based Algorithms

Through this article, we will explore both XGboost Vs Random Forest Tree Based Algorithm by building model and comparing the performance. In machine learning, we mainly deal with two kinds of problems that are classification and regression. There are several different types of algorithms for both tasks. But we need to pick that algorithm whose performance is good on the respective data.

In machine learning, we mainly deal with two kinds of problems that are classification and regression. There are several different types of algorithms for both tasks. But we need to pick that algorithm whose performance is good on the respective data. Ensemble methods like Random Forest, Decision Tree, XGboost algorithms have shown very good results when we talk about classification. These algorithms give high accuracy at fast speed. Both the two algorithms Random Forest and XGboost are majorly used in Kaggle competition to achieve higher accuracy that simple to use. 

Through this article, we will explore both XGboost and Random Forest algorithms and compare their implementation and performance. We will see how these algorithms work and then we will build classification models based on these algorithms on Pima Indians Diabetes Data where we will classify whether the patient is diabetic or not. We will then evaluate both the models and compare the results. The dataset can be downloaded from Kaggle.

What we will learn from the article?

  • What is the Random Forest Algorithm and how does it work?
  • What is XGboost Algorithm and how does it work?
  • A comprehensive study of Random Forest and XGBoost Algorithms
  • Practically comparing Random Forest and XGBoost Algorithms in classification
  1. *What is the Random Forest Algorithm? How does it work? *

The forest is said to robust when there are a lot of trees in the forest. Random Forest is an ensemble technique that is a tree-based algorithm. The process of fitting no decision trees on different subsample and then taking out the average to increase the performance of the model is called “Random Forest”. Suppose we have to go on a vacation to someplace. Before going to the destination we vote for the place where we want to go. Once we have voted for the destination then we choose hotels, etc. And then come back with the final choice of hotel as well. The whole process of getting the vote for the place to the hotel is nothing but a Random Forest Algorithm. This is the way the algorithm works and the reason it is preferred over all other algorithms because of its ability to give high accuracy and to prevent overfitting by making use of more trees. There are several different hyperparameters like no trees, depth of trees, jobs, etc in this algorithm. Check here the Sci-kit documentation for the same. 


developers corner ensemble method gradient boosting machine learning random forest xgboost

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